[Doc]Add English version of documents in docs/cn and api/vision_results (#931)

* 第一次提交

* 补充一处漏翻译

* deleted:    docs/en/quantize.md

* Update one translation

* Update en version

* Update one translation in code

* Standardize one writing

* Standardize one writing

* Update some en version

* Fix a grammer problem

* Update en version for api/vision result

* Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop

* Checkout the link in README in vision_results/ to the en documents

* Modify a title

* Add link to serving/docs/

* Finish translation of demo.md

docs/en/quick_start/models/cpp.md

@@ -1,3 +1,4 @@
+English | [中文](../../../cn/quick_start/models/cpp.md)
 # C++ Deployment
 
 Please make sure the development environment has FastDeploy C++ SDK installed. Refer to [FastDeploy installation](../../build_and_install/) to install the pre-built FastDeploy, or build and install according to your own needs.

docs/en/quick_start/models/python.md

@@ -1,3 +1,5 @@
+English | [中文](../../../cn/quick_start/models/python.md)
+
 # Python Deployment
 
 Make sure that FastDeploy is installed in the development environment. Refer to [FastDeploy Installation](../../build_and_install/) to install the pre-built FastDeploy, or build and install according to your own needs.

docs/en/quick_start/runtime/cpp.md

@@ -1,3 +1,120 @@
-# C++ Deployment
+English | [中文](../../../cn/quick_start/runtime/cpp.md)
+# C++ Inference
 
-coming soon...
+Please check out the FastDeploy C++ deployment library is already in your environment. You can refer to [FastDeploy Installation](../../build_and_install/) to install the pre-compiled FastDeploy, or customize your installation.
+
+This document shows an inference sample on the CPU using the PaddleClas classification model MobileNetV2 as an example.
+
+## 1. Obtaining the Module
+
+```bash
+wget https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz
+tar xvf mobilenetv2.tgz
+```
+
+## 2. Backend Configuration
+
+The following C++ code is saved as `infer_paddle_onnxruntime.cc`.
+
+``` c++
+#include "fastdeploy/runtime.h"
+
+namespace fd = fastdeploy;
+
+int main(int argc, char* argv[]) {
+  std::string model_file = "mobilenetv2/inference.pdmodel";
+  std::string params_file = "mobilenetv2/inference.pdiparams";
+
+  // setup option
+  fd::RuntimeOption runtime_option;
+  runtime_option.SetModelPath(model_file, params_file, fd::ModelFormat::PADDLE);
+  runtime_option.UseOrtBackend();
+  runtime_option.SetCpuThreadNum(12);
+  // init runtime
+  std::unique_ptr<fd::Runtime> runtime =
+      std::unique_ptr<fd::Runtime>(new fd::Runtime());
+  if (!runtime->Init(runtime_option)) {
+    std::cerr << "--- Init FastDeploy Runitme Failed! "
+              << "\n--- Model:  " << model_file << std::endl;
+    return -1;
+  } else {
+    std::cout << "--- Init FastDeploy Runitme Done! "
+              << "\n--- Model:  " << model_file << std::endl;
+  }
+  // init input tensor shape
+  fd::TensorInfo info = runtime->GetInputInfo(0);
+  info.shape = {1, 3, 224, 224};
+
+  std::vector<fd::FDTensor> input_tensors(1);
+  std::vector<fd::FDTensor> output_tensors(1);
+
+  std::vector<float> inputs_data;
+  inputs_data.resize(1 * 3 * 224 * 224);
+  for (size_t i = 0; i < inputs_data.size(); ++i) {
+    inputs_data[i] = std::rand() % 1000 / 1000.0f;
+  }
+  input_tensors[0].SetExternalData({1, 3, 224, 224}, fd::FDDataType::FP32, inputs_data.data());
+
+  //get input name
+  input_tensors[0].name = info.name;
+
+  runtime->Infer(input_tensors, &output_tensors);
+
+  output_tensors[0].PrintInfo();
+  return 0;
+}
+```
+When loading is complete, you can get the following output information indicating the initialized backend and the hardware devices.
+```
+[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init	Runtime initialized with Backend::OrtBackend in device Device::CPU.
+```
+
+## 3. Prepare for CMakeLists.txt
+
+FastDeploy contains several dependencies, it is more complicated to compile directly with `g++` or a compiler, so we recommend to use cmake to compile and configure. The sample configuration is as follows.
+
+```cmake
+PROJECT(runtime_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
+
+# Specify path to the fastdeploy library after downloading and unpacking
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# Add FastDeploy dependency headers
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(runtime_demo ${PROJECT_SOURCE_DIR}/infer_onnx_openvino.cc)
+# Add FastDeploy dependency libraries
+target_link_libraries(runtime_demo ${FASTDEPLOY_LIBS})
+```
+
+## 4. Compile executable program
+
+Open a terminal, go to the directory where `infer_paddle_onnxruntime.cc` and `CMakeLists.txt` are located, and then run:
+
+```bash
+cd examples/runtime/cpp
+mkdir build & cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=$fastdeploy_cpp_sdk
+make -j
+```
+
+```fastdeploy_cpp_sdk``` is path to FastDeploy C++ deployment library.
+
+After compiling, you can get your results by running:
+```bash
+./runtime_demo
+```
+If `error while loading shared libraries: libxxx.so: cannot open shared object file: No such file...`is reported, it means that the path to FastDeploy is not found. You can re-execute the program after adding the  FastDeploy library path to the environment variable by running the following command.
+```bash
+source /Path/to/fastdeploy_cpp_sdk/fastdeploy_init.sh
+```
+
+This sample code is common on all platforms (Windows/Linux/Mac), but the compilation process is only supported on (Linux/Mac),while using msbuild to compile on Windows. Please refer to [FastDeploy C++ SDK on Windows](../../faq/use_sdk_on_windows.md).
+
+## Other Documents
+
+- [Runtime demos on different backends](../../../../examples/runtime/README.md)
+- [Switching hardware and backend for model inference](../../faq/how_to_change_backend.md)

docs/en/quick_start/runtime/python.md

@@ -1,3 +1,54 @@
-# Python Deployment
+English | [中文](../../../cn/quick_start/runtime/python.md)
+# Python Inference
+
+Please check out the FastDeploy is already installed in your environment. You can refer to [FastDeploy Installation](../../build_and_install/) to install the pre-compiled FastDeploy, or customize your installation.
+
+This document shows an inference sample on the CPU using the PaddleClas classification model MobileNetV2 as an example.
+
+## 1. Obtaining the Module
+
+``` python
+import fastdeploy as fd
+
+model_url = "https://bj.bcebos.com/fastdeploy/models/mobilenetv2.tgz"
+fd.download_and_decompress(model_url, path=".")
+```
+
+## 2. Backend Configuration
+
+- For more examples, you can refer to [examples/runtime](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/runtime).
+
+``` python
+option = fd.RuntimeOption()
+
+option.set_model_path("mobilenetv2/inference.pdmodel",
+                      "mobilenetv2/inference.pdiparams")
+
+# **** CPU Configuration ****
+option.use_cpu()
+option.use_ort_backend()
+option.set_cpu_thread_num(12)
+
+# Initialise runtime
+runtime = fd.Runtime(option)
+
+# Get model input name
+input_name = runtime.get_input_info(0).name
+
+# Constructing random data for inference
+results = runtime.infer({
+    input_name: np.random.rand(1, 3, 224, 224).astype("float32")
+})
+
+print(results[0].shape)
+```
+When loading is complete, you can get the following output information indicating the initialized backend and the hardware devices.
+```
+[INFO] fastdeploy/fastdeploy_runtime.cc(283)::Init	Runtime initialized with Backend::OrtBackend in device Device::CPU.
+```
+
+## Other Documents
+
+- [Runtime demos on different backends](../../../../examples/runtime/README.md)
+- [Switching hardware and backend for model inference](../../faq/how_to_change_backend.md)
 
-coming soon...